AI-Driven Innovations in Turbomachinery Flow Modeling and Design Optimization
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".
Deadline for manuscript submissions: 25 September 2025 | Viewed by 46
Special Issue Editors
Interests: aerospace; power system; aerothermodynamics
Special Issues, Collections and Topics in MDPI journals
Interests: heat transfer in gas turbines
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of Computational Fluid Dynamics (CFD) with deep learning techniques has transformed the design, analysis, and optimization of turbomachinery components such as turbines, compressors, and fans. Recent advancements in deep learning are accelerating this transformation, enabling real-time flow field predictions, data-driven surrogate modeling, and intelligent optimization of complex geometries. This Special Issue explores the latest developments in CFD methodologies and their synergy with deep learning to tackle the complex challenges in turbomachinery, focusing on improving efficiency, durability, and sustainability across energy generation, aerospace propulsion, and industrial fluid systems. By combining high-fidelity simulations with neural network-based approaches, researchers are now enabled to solve previously intractable problems, such as transient flow dynamics and multi-scale interactions, while reducing computational costs.
Key themes include the following:
- High-Fidelity Simulations: Advanced turbulence modeling and unsteady flow analysis for complex, multi-phase interactions in rotating machinery, enabling precise performance predictions under extreme operating conditions.
- Design Optimization: AI-driven and adjoint-based optimization frameworks that enhance aerodynamic and thermal performance, addressing multi-objective trade-offs (e.g., efficiency versus noise and emissions).
- Multiphysics Integration: Coupling CFD with structural mechanics, heat transfer, and material science to solve challenges in cooling systems, blade fatigue, and high-temperature operation.
- Advanced Numerical Methods: The use of high-performance computing (HPC), reduced-order modeling (ROM), and adaptive mesh refinement (AMR) to enable scalable and cost-effective simulations.
- Intelligent Mesh Parametrization and Adaptive Generation: Geometry-driven mesh parametrization, topology optimization, and adaptive refinement (AMR), integrated with machine learning to automate mesh quality control and accelerate parametric studies for complex blade profiles and cooling channels.
- Machine Learning and Data-Driven Insights: Hybrid models combining CFD with machine learning for rapid prototyping, uncertainty quantification, and the development of digital twins.
- Sustainable Innovations: CFD-driven solutions for renewable energy systems (e.g., wind and hydro turbines) and low-emission combustion technologies.
This issue bridges the gap between academia and industry by showcasing interdisciplinary research, validated case studies, and emerging tools that push the boundaries of turbomachinery design. Contributions are encouraged to highlight both theoretical breakthroughs and practical applications, paving the way for next-generation, energy-efficient fluid machinery.
Dr. Jianyang Yu
Dr. Wei Du
Guest Editors
Manuscript Submission Information
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Keywords
- turbomachinery
- computational fluid dynamics
- artificial intelligence
- modeling
- simulation
- machine learning
- energy efficiency
- renewable energy systems
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